<b>Full-Domain Architecture for Enterprise Informatization in the Digital Economy</b><b></b>
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Keywords

Digital economy
Enterprise informatization
Full-domain architecture
Configuration analysis
Organizational transformation

How to Cite

Full-Domain Architecture for Enterprise Informatization in the Digital Economy. (2026). International Journal of Frontiers of Modern Synthesis, 1(01), 46-59. https://iakgvllc.org/index.php/IJFMS/article/view/6

Abstract

Today, the evolution of the digital economy has exacerbated the deficiencies in enterprise informatization and fragmented architecture. This study constructs a "global" architecture concept and empirically investigates its implementation path. Based on complex adaptive systems theory, this study employs a hybrid approach, integrating panel data from 426 A-share listed companies and 472 questionnaires. Analysis is conducted using coupling coordination degree, structural equation modeling mediation effects, panel thresholds, and global qualitative comparative analysis configuration models. The results indicate that successful transformation requires coordination at the socio-technical level, not just technological investment. This framework provides data-driven insights for enterprises across various industries, including manufacturing, technological innovation, and services, undergoing digital transformation.

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References

[1] Feng, Y. (2026). Research on the Construction and Practice of the Full-Domain Architecture System for Enterprise Management Informatization under the Digital Economy. Journal of World Economy, 5(2), 17-27.

[2] Chen, Y. (2026). Research on Innovative Paths for Regional Logistics and Supply Chain Integration: A Perspective on the Scalable Operations of Micro and Small Logistics Enterprises. Journal of Intelligence and Engineering Technol0ogy, 1(1), 70-79.

[3] Kane, G. (2019). The technology fallacy: people are the real key to digital transformation. Research-technology management, 62(6), 44-49.

[4] Hao, Z. (2026). Structure-Aware Deep Reinforcement Learning for Latency-Minimal Scheduling of Edge AI Inference on Heterogeneous Cores. Journal of Intelligence and Engineering Technology, 1(1), 50-59.

[5] Huang, H. (2025). Development and Evaluation of a Teacher Training Program in Artificial Intelligence Technology. Journal of Advanced Research in Education, 4(1), 23-31.

[6] Chen, Y. (2025). Analysis of the Technical Application and Effectiveness of Intelligent Algorithms Empowering Regional Logistics Resource Matching. Engineering Frontiers, 1(3).

[7] Feng, Y. (2026). Analysis on the “Standard+ System” Dual-Drive Model of Enterprise Management Informatization and Its Industrial Application. Frontiers in Management Science, 5(2), 41-49.

[8] Huang, H. (2025). Interdisciplinary Collaboration in Educational Informatization Innovation: Importance and Practice. Research and Advances in Education, 4(1), 38-46.

[9] Liu, Y. (2026). Heterogeneous Resource Slot Optimization in Multi-Dimensional Recommendation Landscapes: A Submodular Constrained Framework with Cross-Space Spillover Effects. Journal of Progress in Engineering and Physical Science, 5(1), 26-31.

[10] Meng, S. (2026). Bridging Research and Market Adoption in Artificial Intelligence: an Investment-Driven Framework for Commercializing AI Security Technologies. Academic Journal of Sociology and Management, 4(3), 12-19.

[11] Barr, J. (2013). Makers: The New Industrial Revolution by Chris Anderson: New York, NY: Crown Business, 2012, 257 pp., $26.00, ISBN 978-0-307-72905-5.

[12] Meng, S. (2026). Strategic Management of Large-Scale Innovation Funds: a Framework for Accelerating Technology Commercialization Across Emerging Industries. Journal of Economic Theory and Business Management, 3(2), 11-15.

[13] Nie, X. (2025). Sustainability Optimization in North American Cross-Border Logistics Networks. Journal of World Economy, 4(6), 66-73.

[14] Zhang, N. (2026). Research on the Construction of a Multi-Format Integrated Management System for Small, Medium and Micro-Sized Enterprises. Frontiers in Management Science, 5(2), 9-18.

[15] Meng, S. (2026). Accelerating Commercial Space Technology Commercialization Through Government-Guided Industrial Investment Funds: a Case Study of CAS Space and China's Emerging Aerospace Ecosystem. Journal of Economic Theory and Business Management, 3(2), 1-5.

[16] Ross, J. W., Beath, C. M., & Mocker, M. (2019). Designed for digital: How to architect your business for sustained success. Mit Press.

[17] Liu, Y. (2026). Cascading Resilience Through Predictive Multi-Dimensional Safeguards: System Stability Architecture for Billion-Scale Concurrent Platforms. Innovation in Science and Technology, 5(1), 35-45.

[18] Hao, Z. (2025). Task Affinity-Aware Scheduling for Multi-Core Edge Devices in Autonomous Vehicles. Engineering Frontiers, 1(2).

[19] Hao, Z. (2026). Energy Efficient Multi Core Task Scheduling for Real Time Edge AI Systems: A Latency Aware Approach. International Journal of Advance in Applied Science Research, 5(3), 1-14.

[20] Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2015). Strategy, not technology, drives digital transformation. MIT Sloan management review.

[21] Feng, Y. (2026). Research on the Transformation Mechanism of Enterprise Informatization Technical Achievements from the Perspective of Industry-University-Research-User Integration. Innovation in Science and Technology, 5(2), 45-53.

[22] Chen, Y. (2025). Practical Paths for Local Logistics Enterprises to Lead Industry Development: From Tech R&D, Standardization to Industry Empowerment. Engineering Frontiers, 1(4).

[23] Hao, Z. (2025). Fault-Tolerant Real-Time Scheduling for Edge AI in US Critical Infrastructure. Engineering Frontiers, 1(4).

[24] Dong, X., & McIntyre, S. H. (2014). The second machine age: work, progress, and prosperity in a time of brilliant technologies.

[25] Shengtao, L. (2025). Machine Learning-Based Logistics Network Optimization Algorithm. Academic Journal of Computing & Inform0ation Science, 8(5), 46-54.

[26] Hao, Z. (2026). Low-Overhead Scheduling for Real-Time AI Workloads on Multi-Core Edge Chips. International Journal of Advance in Applied Science Research, 5(3), 15-25.

[27] Hao, Z. (2026). Dynamic Task Prioritization for Edge AI in Smart Cities: Balancing Latency and Energy Efficiency. Journal of Intelligence and Engineering Technology, 1(1), 60-69.

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